{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "A minimal implementation of Monte Carlo tree search (MCTS) in Python 3\n",
    "Luke Harold Miles, July 2019, Public Domain Dedication\n",
    "See also https://en.wikipedia.org/wiki/Monte_Carlo_tree_search\n",
    "https://gist.github.com/qpwo/c538c6f73727e254fdc7fab81024f6e1\n",
    "\"\"\"\n",
    "from collections import defaultdict\n",
    "import math\n",
    "\n",
    "\n",
    "class MCTS:\n",
    "    \"\"\"Monte Carlo tree searcher. First rollout the tree then choose\n",
    "    a move.\n",
    "    \"\"\"\n",
    "    def __init__(self, exploration_weight=1):\n",
    "        self.Q = defaultdict(int)  # total reward of each node\n",
    "        self.N = defaultdict(int)  # total visit count for each node\n",
    "        self.children = dict()  # children of each node\n",
    "        self.exploration_weight = exploration_weight\n",
    "\n",
    "    def choose(self, node):\n",
    "        \"\"\"Choose the best successor of node. (Choose a move in the game)\n",
    "        \"\"\"\n",
    "        if node.is_terminal():\n",
    "            raise RuntimeError(f\"choose called on terminal node {node}\")\n",
    "\n",
    "        if node not in self.children:\n",
    "            return node.find_random_child()\n",
    "\n",
    "        def score(n):\n",
    "            if self.N[n] == 0:\n",
    "                return float(\"-inf\")  # avoid unseen moves\n",
    "            return self.Q[n] / self.N[n]  # average reward\n",
    "\n",
    "        return max(self.children[node], key=score)\n",
    "\n",
    "    def do_rollout(self, node):\n",
    "        \"\"\"Make the tree one layer better. (Train for one iteration.)\n",
    "        \"\"\"\n",
    "        path = self._select(node)\n",
    "        leaf = path[-1]\n",
    "        self._expand(leaf)\n",
    "        reward = self._simulate(leaf)\n",
    "        self._backpropagate(path, reward)\n",
    "\n",
    "    def _select(self, node):\n",
    "        \"\"\"Find an unexplored descendent of `node`\n",
    "        \"\"\"\n",
    "        path = []\n",
    "        while True:\n",
    "            path.append(node)\n",
    "            if node not in self.children or not self.children[node]:\n",
    "                # node is either unexplored or terminal\n",
    "                return path\n",
    "            unexplored = self.children[node] - self.children.keys()\n",
    "            if unexplored:\n",
    "                n = unexplored.pop()\n",
    "                path.append(n)\n",
    "                return path\n",
    "            node = self._uct_select(node)  # descend a layer deeper\n",
    "\n",
    "    def _expand(self, node):\n",
    "        \"\"\"Update the `children` dict with the children of `node`\n",
    "        \"\"\"\n",
    "        if node in self.children:\n",
    "            return  # already expanded\n",
    "        self.children[node] = node.find_children()\n",
    "\n",
    "    def _simulate(self, node):\n",
    "        \"\"\"Returns the reward for a random simulation (to completion)\n",
    "        of `node`\n",
    "        \"\"\"\n",
    "        invert_reward = True\n",
    "        while True:\n",
    "            if node.is_terminal():\n",
    "                reward = node.reward()\n",
    "                return 1 - reward if invert_reward else reward\n",
    "            node = node.find_random_child()\n",
    "            invert_reward = not invert_reward\n",
    "\n",
    "    def _backpropagate(self, path, reward):\n",
    "        \"\"\"Send the reward back up to the ancestors of the leaf\n",
    "        \"\"\"\n",
    "        for node in reversed(path):\n",
    "            self.N[node] += 1\n",
    "            self.Q[node] += reward\n",
    "            reward = 1 - reward  # 1 for me is 0 for my enemy, and vice versa\n",
    "\n",
    "    def _uct_select(self, node):\n",
    "        \"\"\"Select a child of node, balancing exploration & exploitation\n",
    "        \"\"\"\n",
    "        assert all(n in self.children for n in self.children[node])\n",
    "\n",
    "        log_N_vertex = math.log(self.N[node])\n",
    "\n",
    "        def uct(n):\n",
    "            \"\"\"Upper confidence bound for trees\n",
    "            \"\"\"\n",
    "            return self.Q[n] / self.N[n] + self.exploration_weight * math.sqrt(\n",
    "                log_N_vertex / self.N[n]\n",
    "            )\n",
    "\n",
    "        return max(self.children[node], key=uct)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from abc import ABC, abstractmethod\n",
    "\n",
    "\n",
    "class Node(ABC):\n",
    "    \"\"\"\n",
    "    A representation of a single board state.\n",
    "    MCTS works by constructing a tree of these Nodes.\n",
    "    Could be e.g. a chess or checkers board state.\n",
    "    \"\"\"\n",
    "\n",
    "    @abstractmethod\n",
    "    def find_children(self):\n",
    "        \"All possible successors of this board state\"\n",
    "        return set()\n",
    "\n",
    "    @abstractmethod\n",
    "    def find_random_child(self):\n",
    "        \"Random successor of this board state (for more efficient simulation)\"\n",
    "        return None\n",
    "\n",
    "    @abstractmethod\n",
    "    def is_terminal(self):\n",
    "        \"Returns True if the node has no children\"\n",
    "        return True\n",
    "\n",
    "    @abstractmethod\n",
    "    def reward(self):\n",
    "        \"Assumes `self` is terminal node. 1=win, 0=loss, .5=tie, etc\"\n",
    "        return 0\n",
    "\n",
    "    @abstractmethod\n",
    "    def __hash__(self):\n",
    "        \"Nodes must be hashable\"\n",
    "        return 123456789\n",
    "\n",
    "    @abstractmethod\n",
    "    def __eq__(node1, node2):\n",
    "        \"Nodes must be comparable\"\n",
    "        return True\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "An example implementation of the abstract Node class for use in MCTS\n",
    "If you run this file then you can play against the computer.\n",
    "A tic-tac-toe board is represented as a tuple of 9 values, each either None,\n",
    "True, or False, respectively meaning 'empty', 'X', and 'O'.\n",
    "The board is indexed by row:\n",
    "0 1 2\n",
    "3 4 5\n",
    "6 7 8\n",
    "For example, this game board\n",
    "O - X\n",
    "O X -\n",
    "X - -\n",
    "corrresponds to this tuple:\n",
    "(False, None, True, False, True, None, True, None, None)\n",
    "\"\"\"\n",
    "\n",
    "from collections import namedtuple\n",
    "from random import choice\n",
    "#from monte_carlo_tree_search import MCTS, Node\n",
    "\n",
    "_TTTB = namedtuple(\"TicTacToeBoard\", \"tup turn winner terminal\")\n",
    "\n",
    "# Inheriting from a namedtuple is convenient because it makes the class\n",
    "# immutable and predefines __init__, __repr__, __hash__, __eq__, and others\n",
    "class TicTacToeBoard(_TTTB, Node):\n",
    "    def find_children(board):\n",
    "        if board.terminal:  # If the game is finished then no moves can be made\n",
    "            return set()\n",
    "        # Otherwise, you can make a move in each of the empty spots\n",
    "        return {\n",
    "            board.make_move(i) for i, value in enumerate(board.tup) if value is None\n",
    "        }\n",
    "\n",
    "    def find_random_child(board):\n",
    "        if board.terminal:\n",
    "            return None  # If the game is finished then no moves can be made\n",
    "        empty_spots = [i for i, value in enumerate(board.tup) if value is None]\n",
    "        return board.make_move(choice(empty_spots))\n",
    "\n",
    "    def reward(board):\n",
    "        if not board.terminal:\n",
    "            raise RuntimeError(f\"reward called on nonterminal board {board}\")\n",
    "        if board.winner is board.turn:\n",
    "            # It's your turn and you've already won. Should be impossible.\n",
    "            raise RuntimeError(f\"reward called on unreachable board {board}\")\n",
    "        if board.turn is (not board.winner):\n",
    "            return 0  # Your opponent has just won. Bad.\n",
    "        if board.winner is None:\n",
    "            return 0.5  # Board is a tie\n",
    "        # The winner is neither True, False, nor None\n",
    "        raise RuntimeError(f\"board has unknown winner type {board.winner}\")\n",
    "\n",
    "    def is_terminal(board):\n",
    "        return board.terminal\n",
    "\n",
    "    def make_move(board, index):\n",
    "        tup = board.tup[:index] + (board.turn,) + board.tup[index + 1 :]\n",
    "        turn = not board.turn\n",
    "        winner = _find_winner(tup)\n",
    "        is_terminal = (winner is not None) or not any(v is None for v in tup)\n",
    "        return TicTacToeBoard(tup, turn, winner, is_terminal)\n",
    "\n",
    "    def to_pretty_string(board):\n",
    "        to_char = lambda v: (\"X\" if v is True else (\"O\" if v is False else \" \"))\n",
    "        rows = [\n",
    "            [to_char(board.tup[3 * row + col]) for col in range(3)] for row in range(3)\n",
    "        ]\n",
    "        return (\n",
    "            \"\\n  1 2 3\\n\"\n",
    "            + \"\\n\".join(str(i + 1) + \" \" + \" \".join(row) for i, row in enumerate(rows))\n",
    "            + \"\\n\"\n",
    "        )\n",
    "\n",
    "\n",
    "def play_tic_tac_toe():\n",
    "    tree = MCTS()\n",
    "    board = new_tic_tac_toe_board()\n",
    "    print(board.to_pretty_string())\n",
    "    while True:\n",
    "        row_col = input(\"enter row,col: \")\n",
    "        row, col = map(int, row_col.split(\",\"))\n",
    "        index = 3 * (row - 1) + (col - 1)\n",
    "        if board.tup[index] is not None:\n",
    "            raise RuntimeError(\"Invalid move\")\n",
    "        board = board.make_move(index)\n",
    "        print(board.to_pretty_string())\n",
    "        if board.terminal:\n",
    "            break\n",
    "        # You can train as you go, or only at the beginning.\n",
    "        # Here, we train as we go, doing fifty rollouts each turn.\n",
    "        for _ in range(50):\n",
    "            tree.do_rollout(board)\n",
    "        board = tree.choose(board)\n",
    "        print(board.to_pretty_string())\n",
    "        if board.terminal:\n",
    "            break\n",
    "\n",
    "\n",
    "def _winning_combos():\n",
    "    for start in range(0, 9, 3):  # three in a row\n",
    "        yield (start, start + 1, start + 2)\n",
    "    for start in range(3):  # three in a column\n",
    "        yield (start, start + 3, start + 6)\n",
    "    yield (0, 4, 8)  # down-right diagonal\n",
    "    yield (2, 4, 6)  # down-left diagonal\n",
    "\n",
    "\n",
    "def _find_winner(tup):\n",
    "    \"Returns None if no winner, True if X wins, False if O wins\"\n",
    "    for i1, i2, i3 in _winning_combos():\n",
    "        v1, v2, v3 = tup[i1], tup[i2], tup[i3]\n",
    "        if False is v1 is v2 is v3:\n",
    "            return False\n",
    "        if True is v1 is v2 is v3:\n",
    "            return True\n",
    "    return None\n",
    "\n",
    "\n",
    "def new_tic_tac_toe_board():\n",
    "    return TicTacToeBoard(tup=(None,) * 9, turn=True, winner=None, terminal=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: python-chess in /Users/ben/anaconda3/lib/python3.6/site-packages (0.31.4)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install python-chess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import chess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hashlib\n",
    "import copy\n",
    "\n",
    "_TTTB = namedtuple('ChessGame', 'board')\n",
    "\n",
    "# Inheriting from a namedtuple is convenient because it makes the class\n",
    "# immutable and predefines __init__, __repr__, __hash__, __eq__, and others\n",
    "class ChessGame(_TTTB, Node):\n",
    "    def find_children(self):\n",
    "        if self.is_terminal():\n",
    "            return set()\n",
    "        return {\n",
    "            self.make_move(m) for m in self.board.legal_moves\n",
    "        }\n",
    "\n",
    "    def find_random_child(self):\n",
    "        if self.is_terminal():\n",
    "            return None  # If the game is finished then no moves can be made\n",
    "        moves = list(self.board.legal_moves)\n",
    "        m = choice(moves)\n",
    "        # print(m)\n",
    "        return self.make_move(m)\n",
    "\n",
    "    def player_win(self, turn):\n",
    "        if self.board.result() == '1-0' and turn:\n",
    "            return True\n",
    "        if self.board.result() == '0-1' and not turn:\n",
    "            return True\n",
    "        return False\n",
    "\n",
    "    def debug(self):\n",
    "        print('\\n{self.board}')\n",
    "        print([str(m) for m in self.board.move_stack])\n",
    "    \n",
    "    def reward(self):\n",
    "        if not self.is_terminal():\n",
    "            raise RuntimeError(f'reward called on nonterminal board {self.board}')\n",
    "        if self.board.result() == '1/2-1/2':\n",
    "            return 0.5\n",
    "        if self.player_win(not self.board.turn):\n",
    "            return 0.0  # Your opponent has just won. Bad.\n",
    "        if self.player_win(self.board.turn):\n",
    "            # It's your turn and you've already won. Should be impossible.\n",
    "            raise RuntimeError(f'reward called on unreachable board \\n{self.board}')\n",
    "        # The winner is neither True, False, nor None\n",
    "        #return random.random()\n",
    "        # return 0.0\n",
    "        self.debug()\n",
    "        raise RuntimeError(f'board has unknown winner type!')\n",
    "\n",
    "    def make_move(self, move):\n",
    "        child = self.board.copy()\n",
    "        child.push(move)\n",
    "        return ChessGame(child)\n",
    "    \n",
    "    def is_terminal(self):\n",
    "        return self.board.is_game_over()\n",
    "    \n",
    "    def __hash__(self):\n",
    "        \"Nodes must be hashable\"\n",
    "        return int(\n",
    "            hashlib.md5(\n",
    "                self.board.fen().encode('utf-8')\n",
    "            ).hexdigest()[:8],\n",
    "            16\n",
    "        )\n",
    "    \n",
    "    def __eq__(self, other):\n",
    "        \"Nodes must be comparable\"\n",
    "        return self.__hash__() == other.__hash__()\n",
    "\n",
    "    def __repr__(self):\n",
    "        return '\\n' + str(self.board)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display\n",
    "import chess\n",
    "import chess.svg\n",
    "\n",
    "\n",
    "def play_chess():\n",
    "    tree = MCTS()\n",
    "    game = ChessGame(chess.Board())\n",
    "    display(chess.svg.board(board=game.board, size=300))\n",
    "\n",
    "    while True:\n",
    "        move_str = input('enter move: ')\n",
    "        move = chess.Move.from_uci(move_str)\n",
    "        if move not in list(game.board.legal_moves):\n",
    "            raise RuntimeError('Invalid move')\n",
    "        game = game.make_move(move)\n",
    "        display(chess.svg.board(board=game.board, size=300))\n",
    "        if game.is_terminal():\n",
    "            break\n",
    "        # You can train as you go, or only at the beginning.\n",
    "        # Here, we train as we go, doing fifty rollouts each turn.\n",
    "        for _ in range(50):\n",
    "            tree.do_rollout(game)\n",
    "        game = tree.choose(game)\n",
    "        print(game)\n",
    "        if game.is_terminal():\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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      "enter move: a2a4\n"
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      ". . . . . . . .\n",
      ". P P P P P P P\n",
      "R N B Q K B N R\n"
     ]
    }
   ],
   "source": [
    "play_chess()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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